Is there a role for pharmacists in multidisciplinary health-care teams at community outreach events for the homeless?

Homelessness is a significant public health problem. It is well-documented that people experiencing homelessness exhibit more serious illnesses and have poorer health than the general population. The provision of services and interventions by health-care professionals, including pharmacists, may make a simple yet important contribution to improved health outcomes in those experiencing homelessness, but evidence of roles and interventions is limited and variable. In Australia, the Queensland University of Technology Health Clinic connects with the homeless community by taking part in community outreach events. This paper provides details of one such event, as well as the roles, interventions and experiences of pharmacists. Participation and inclusion of pharmacists in a multidisciplinary health-care team approach at homeless outreach events should be supported and encouraged.

Up close and personal with those experiencing homelessness: Pharmacist involvement in a multidisciplinary healthcare team

Introduction. The Brisbane City Council holds a biannual Homeless Connect event which brings together business and community groups on one day to provide free services to people experiencing or at risk of homelessness. Pharmacists were involved in this initiative and provided health services in a multidisciplinary healthcare environment building on the lessons of previous Homeless Connect events (Chan et al, 2015) Aims. To explore pharmacists reflections on their role in a multidisciplinary healthcare team providing services at a community outreach event for those experiencing homelessness. Methods. The pharmacists (n=2) documented the types of services provided during the Homeless Connect event. A semi-structured interview was conducted post-event to investigate barriers, facilitators and changes that would be recommended for future events. Their perceptions of their role in the multidisciplinary healthcare team were also explored. Results. Primarily, the services provided included delivery of primary healthcare, advice on accessing cost effective pharmacy services and addressing medication enquiries. The pharmacists also provided moisturiser samples and health information leaflets. Interdisciplinary referrals were primarily between the pharmacists and podiatrists; no pharmacist-medical practitioner referrals occurred. The pharmacists did believe they had a positive role in this health initiative but improvements could be implemented to improve the delivery of these services in future events. Discussion. Pharmacists can play an important role in providing services to people experiencing or at risk of homelessness and the overall experience was positive for the pharmacists. They were able to integrate into a multidisciplinary healthcare team in this setting but strategies for further collaboration were identified. The possibility of involving pharmacy students in future events was identified.

Business process performance mining with staged process flows

Existing process mining techniques provide summary views of the overall process performance over a period of time, allowing analysts to identify bottlenecks and associated performance issues. However, these tools are not de- signed to help analysts understand how bottlenecks form and dissolve over time nor how the formation and dissolution of bottlenecks – and associated fluctua- tions in demand and capacity – affect the overall process performance. This paper presents an approach to analyze the evolution of process performance via a notion of Staged Process Flow (SPF). An SPF abstracts a business process as a series of queues corresponding to stages. The paper defines a number of stage character- istics and visualizations that collectively allow process performance evolution to be analyzed from multiple perspectives. The approach has been implemented in the ProM process mining framework. The paper demonstrates the advantages of the SPF approach over state-of-the-art process performance mining tools using two real-life event logs publicly available.

Corporate fraud culture: Re-examining the corporate governance and performance relation

We analyse the corporate governance and performance relation, when conditioning on corporate fraud, for fraud firms during 2000 – 2007. Fraud firms are identified as either self- reported fraud events, or subject to regulatory investigation. We use the inverse Mills ratio procedure to account for firms' (unobservable) fraud culture in the dynamic system GMM model of the performance- governance relation. We find that corporate governance is an endogenously determined characteristic that has no causal impact on firm performance when conditioning on fraud. Fraud is a significant regulatory event but its overall economic impact at the firm level is highly variable.

Minimizing overprocessing waste in business processes via predictive activity ordering

Overprocessing waste occurs in a business process when effort is spent in a way that does not add value to the customer nor to the business. Previous studies have identied a recurrent overprocessing pattern in business processes with so-called "knockout checks", meaning activities that classify a case into "accepted" or "rejected", such that if the case is accepted it proceeds forward, while if rejected, it is cancelled and all work performed in the case is considered unnecessary. Thus, when a knockout check rejects a case, the effort spent in other (previous) checks becomes overprocessing waste. Traditional process redesign methods propose to order knockout checks according to their mean effort and rejection rate. This paper presents a more fine-grained approach where knockout checks are ordered at runtime based on predictive machine learning models. Experiments on two real-life processes show that this predictive approach outperforms traditional methods while incurring minimal runtime overhead.

Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case,and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.

Multifaceted Modelling of Complex Business Enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.

Evaluating business process compliance management frameworks

This research contributes a formal framework to evaluate whether existing CMFs can model and reason about various types of normative requirements. The framework can be used to determine the level of coverage of concepts provided by CMFs, establish mappings between CMF languages and the semantics for the normative concepts and evaluate the suitability of a CMF for issuing a certification of compliance. The developed framework is independent of any specific formalism and it has been formally defined and validated through the examples of such mappings of CMFs.

Traffic and nucleation events as main sources of ultrafine particles in high-insolation developed world cities

Road traffic emissions are often considered the main source of ultrafine particles (UFP, diameter smaller than 100 nm) in urban environments. However, recent studies worldwide have shown that - in high-insolation urban regions at least - new particle formation events can also contribute to UFP. In order to quantify such events we systematically studied three cities located in predominantly sunny environments: Barcelona (Spain), Madrid (Spain) and Brisbane (Australia). Three long term datasets (1-2 years) of fine and ultrafine particle number size distributions (measured by SMPS, Scanning Mobility Particle Sizer) were analysed. Compared to total particle number concentrations, aerosol size distributions offer far more information on the type, origin and atmospheric evolution of the particles. By applying k-Means clustering analysis, we categorized the collected aerosol size distributions in three main categories: “Traffic” (prevailing 44-63% of the time), “Nucleation” (14-19%) and “Background pollution and Specific cases” (7-22%). Measurements from Rome (Italy) and Los Angeles (California) were also included to complement the study. The daily variation of the average UFP concentrations for a typical nucleation day at each site revealed a similar pattern for all cities, with three distinct particle bursts. A morning and an evening spike reflected traffic rush hours, whereas a third one at midday showed nucleation events. The photochemically nucleated particles burst lasted 1-4 hours, reaching sizes of 30-40 nm. On average, the occurrence of particle size spectra dominated by nucleation events was 16% of the time, showing the importance of this process as a source of UFP in urban environments exposed to high solar radiation. On average, nucleation events lasting for 2 hours or more occurred on 55% of the days, this extending to >4hrs in 28% of the days, demonstrating that atmospheric conditions in urban environments are not favourable to the growth of photochemically nucleated particles. In summary, although traffic remains the main source of UFP in urban areas, in developed countries with high insolation urban nucleation events are also a main source of UFP. If traffic-related particle concentrations are reduced in the future, nucleation events will likely increase in urban areas, due to the reduced urban condensation sinks.

Lower preoperative quality of life increases postoperative risk of adverse events in women with endometrial cancer: results from the LACE trial

Objective: To examine the association between preoperative quality of life (QoL) and postoperative adverse events in women treated for endometrial cancer. Methods: 760 women with apparent Stage I endometrial cancer were randomised into a clinical trial evaluating laparoscopic versus open surgery. This analysis includes women with preoperative QoL measurements, from the Functional Assessment of Cancer Therapy- General (FACT-G) questionnaire, and who were followed up for at least 6 weeks after surgery (n=684). The outcomes for this study were defined as (1) the occurrence of moderate to severe AEs adverse events within 6 months (Common Toxicology Criteria (CTC) grade ≥3); and (2) any Serious Adverse Event (SAE). The association between preoperative QoL and the occurrence of AE was examined, after controlling for baseline comorbidity and other factors. Results: After adjusting for other factors, odds of occurrence of AE of CTC grade ≥3 were significantly increased with each unit decrease in baseline FACT-G score (OR=1.02, 95% CI 1.00-1.03, p=0.030), which was driven by physical well-being (PWB) (OR=1.09, 95% CI 1.04-1.13, p=0.0002) and functional well-being subscales (FWB) (OR=1.04, 95% CI 1.00-1.07, p=0.035). Similarly, odds of SAE occurrence were significantly increased with each unit decrease in baseline FACT-G score (OR=1.02, 95% CI 1.01-1.04, p=0.011), baseline PWB (OR=1.11, 95% CI 1.06-1.16, p<0.0001) or baseline FWB subscales (OR=1.05, 95% CI 1.01-1.10, p=0.0077). Conclusion: Women with early endometrial cancer presenting with lower QoL prior to surgery are at higher risk of developing a serious adverse event following surgery. Funding: Cancer Council Queensland, Cancer Council New South Wales, Cancer Council Victoria, Cancer Council, Western Australia; NHMRC project grant 456110; Cancer Australia project grant 631523; The Women and Infants Research Foundation, Western Australia; Royal Brisbane and Women’s Hospital Foundation; Wesley Research Institute; Gallipoli Research Foundation; Gynetech; TYCO Healthcare, Australia; Johnson and Johnson Medical, Australia; Hunter New England Centre for Gynaecological Cancer; Genesis Oncology Trust; and Smart Health Research Grant QLD Health.

Complete and interpretable conformance checking of business processes

This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a business process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the process model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by folding the input event log into an event structure, unfolding the process model into another event structure, and comparing the two event structures via an error-correcting synchronized product. Each behavioral difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method scales up to real-life datasets while producing more concise and higher-level difference descriptions than state-of-the-art conformance checking methods.

Gottschalk Family on porch of Kurhaus Brunig; Norderney, Germany. Business Establishments; Hotels

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Crowd lined up in the street for the going-out-of-business sale at Meyerhof am Platze Store; Hildesheim, Germany. Business Establishments; Stores

Loaned for duplication by Renate Feuchtwanger